Environmental Justice (cont.)

Redlining

Dr. Alicia M. Rich

Tue Sep 2, 2025

History

The New Deal

FDR established the Home Owners’ Loan Corporation in 1933 to help homeowners who were at risk of losing their homes to foreclosure
They primarily designed the program to provide housing to white, middle-class, lower-middle-class families
- African-Americans and other people of color were left out of the new suburban communities — and pushed instead into urban housing projects.

The Federal Housing Administration

established through the National Housing Act of 1934

Maps of every metropolitan area in the country
- Color-coded by where it was safe to insure mortgages
  - African-American neighborhoods usually rated with the highest risk (colored red)

The Federal Housing Administration

“Incompatible racial groups should not be permitted to live in the same communities.” – Underwriting Manual, the Federal Housing Administration

The FHA subsidized builders who were mass-producing entire subdivisions for whites
- The Underwriting Manual of the FHA explicitly forbid racial minorities from living in those communities

Omaha’s History

Contemporary Consequences

U.S. Census Data & Redlining History

Baltimore, MA

Values shown as difference from the City's Overall Average

U.S. Census Data & Redlining History

Dallas, TX

Values shown as difference from the City's Overall Average

U.S. Census Data & Redlining History

Kansas City, MO

Values shown as difference from the City's Overall Average

National Community Reinvestment Coalition

The National Community Reinvestment Coalition investigated the HOLC’s lasting impact across 12,833 US Census tracts
- Compared their recent social and economic conditions with city-level measures of segregation and economic inequality

Method of calculating the neighborhood-level historic redlining score.

Key Findings

Hypothesized pathways linking redlining and policies of segregation to current health outcomes at the neighborhood level. (After Krieger, 2020)

Greater historic redlining is related to current neighborhood characteristics, including increased minority presence, higher prevalence of poverty and greater social vulnerability.
There are statistically significant associations between greater redlining and general indicators of population health, including increased prevalence of poor mental health and lower life expectancy at birth.

Data from the residential security maps created by the HOLC’s City Survey program is hosted by the University of Richmond Digital Scholarship Lab as part of their “Mapping Inequality: Redlining in New Deal America” project.
The data includes scans of the original residential security maps, along with examiner grading sheets and digitized shapefiles of the neighborhoods.
The shapefiles are georectified and suitable for projection and use in geographic information systems (GIS). This archive is an extensive and expanding repository of the maps for cities throughout the United States, and served as our primary source material.
census tract level data from the 2019 release of the CDC’s “500 Cities: Local data for better health,” which included small-area estimates from 2016 and 2017, was used as a primary source for most of the health-outcome related variables.
Other data utilized included the CDC’s “small-area life expectancy estimates project” for 2010-2015 and U.S. Census American Community Survey five-year (ACS 5-year) data estimates for 2013-2017.

Key Findings

Results of ANOVA with Games-Howell post-hoc test for significance of the differences – Health indicators with historic disinvestment score divided by quartile nationally. Quartiles 4 and 1 differences only.

There are statistically significant associations between greater redlining and pre-existing conditions for heightened risk of morbidity in COVID-19 patients like asthma, COPD, diabetes, hypertension, high cholesterol, kidney disease, obesity and stroke.
Differences in life expectancy vary greatly among cities: from 14.7 years less in redlined neighborhoods of Rochester, Minnesota, to a 1.3 year greater life expectancy in redlined neighborhoods of Ogden, Utah, which has experienced considerable growth and urban development since 1940.

Data from the residential security maps created by the HOLC’s City Survey program is hosted by the University of Richmond Digital Scholarship Lab as part of their “Mapping Inequality: Redlining in New Deal America” project.
The data includes scans of the original residential security maps, along with examiner grading sheets and digitized shapefiles of the neighborhoods.
The shapefiles are georectified and suitable for projection and use in geographic information systems (GIS). This archive is an extensive and expanding repository of the maps for cities throughout the United States, and served as our primary source material.
census tract level data from the 2019 release of the CDC’s “500 Cities: Local data for better health,” which included small-area estimates from 2016 and 2017, was used as a primary source for most of the health-outcome related variables.
Other data utilized included the CDC’s “small-area life expectancy estimates project” for 2010-2015 and U.S. Census American Community Survey five-year (ACS 5-year) data estimates for 2013-2017.
Results of the One-Way ANOVA Games-Howell post-hoc test for difference in the means of the first and fourth quartiles are presented in Table 3.
The table shows the difference of the means for each variable contrasting the highest (fourth) and lowest (first) quartiles of historic redlining score.
The mean values for percent minority, percent poverty and SVI were all statistically significantly higher in tracts with the highest historic redlining score compared to tracts with the lowest historic redlining score.
As expected, average median age, percent of population age 65 years and older and life expectancy are all statistically significantly lower in tracts with the highest historic redlining score.
Full results of mean contrasts between all quartiles by sociodemographic characteristics and COVID-19 health risk factors are available in the Appendix.

Key Recommendations

Restore the Affirmatively Further Fair Housing (AFFH) rule. This rule encouraged communities to identify and reduce concentrated areas of poverty.
Support inclusionary zoning that adds affordable housing choices outside of the redlined sections of the city and promotes the reduction of concentrated areas of poverty.
Expand the Community Reinvestment Act (CRA) to include non-banks and to modernize how this law is implemented.
Encourage locally relevant solutions such as expanded Section 8 and rent control/vouchers. Protect public housing.

Exploring the NCRC Data

Minneapolis’s Redlining Legacy

The map on the left = redlining map made by the Home Owners Loan Corporation during the 1930s.

The map on the right = the U.S. Centers for Disease Control and Prevention 2018 Social Vulnerability Index (SVI) scores for census tracts. - widely used to assess a community’s capacity to prepare for, respond to, and recover from human and natural disasters. - Key factors in how hard it may be impacted by a disaster: - The social and economic resources available to a community - community’s underlying vulnerabilities

Between the maps = diagram

The rectangles on the left are color-coded by grade from the 1930s HOLC map
On the right, census tracts are plotted by their SVI score.
The paths between them connect the same areas across time and show continuities and changes.

Charts on the bottom

Each dot represents a census tract or, if a HOLC area is shown, an estimation of the overlapping census tract areas.

Pittsburgh’s Redlining Legacy

The map on the left = redlining map made by the Home Owners Loan Corporation during the 1930s.

Between the maps = diagram

The rectangles on the left are color-coded by grade from the 1930s HOLC map
On the right, census tracts are plotted by their SVI score.
The paths between them connect the same areas across time and show continuities and changes.

Charts on the bottom

Each dot represents a census tract or, if a HOLC area is shown, an estimation of the overlapping census tract areas.

Cleveland’s Redlining Legacy

The map on the left = redlining map made by the Home Owners Loan Corporation during the 1930s.

Between the maps = diagram

The rectangles on the left are color-coded by grade from the 1930s HOLC map
On the right, census tracts are plotted by their SVI score.
The paths between them connect the same areas across time and show continuities and changes.

Charts on the bottom

Each dot represents a census tract or, if a HOLC area is shown, an estimation of the overlapping census tract areas.

Omaha’s Redlining Legacy

Map Link

Scan to open on your device:

QR code to map

The map on the left = redlining map made by the Home Owners Loan Corporation during the 1930s.

Between the maps = diagram

The rectangles on the left are color-coded by grade from the 1930s HOLC map
On the right, census tracts are plotted by their SVI score.
The paths between them connect the same areas across time and show continuities and changes.

Charts on the bottom

Each dot represents a census tract or, if a HOLC area is shown, an estimation of the overlapping census tract areas.

Discussion

Scan to open on your device:

QR code to map

Discuss the following questions in small groups first and then as a class:

Do you see areas with a high “health vulnerability” score today that were once rated “D” (hazardous) on redlining maps?

What about areas with lower vulnerability now that were “A” or “B”?

How might redlining policies from the 1930s still influence health outcomes a neighborhood today?

Discussion

Scan to open on your device:

QR code to map

Discuss the following questions in small groups first and then as a class:

Based on the map, where do you think people might have more or fewer healthcare options (clinics, hospitals, pharmacies)?
If you were proposing a public health study on one of these neighborhoods, what health risk(s) or chronic illness(es) might you want to investigate first, and why?

If you were a public health official in this city, which two neighborhoods would you prioritize for intervention?
- What specific strategies would you consider (e.g., environmental cleanup, healthcare investment, housing programs)?

Final Discussion

How does seeing the map make you think differently about the relationship between “place” and “health”?